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| import numpy as np | |
| import pandas as pd | |
| import datasets | |
| import streamlit as st | |
| from streamlit_cytoscapejs import st_cytoscapejs | |
| import networkx as nx | |
| st.set_page_config(layout='wide') | |
| # parse out gene_ids from URL query args to it's possible to link to this page | |
| query_params = st.query_params | |
| if "gene_ids" in query_params.keys(): | |
| input_gene_ids = query_params["gene_ids"] | |
| else: | |
| input_gene_ids = "CNAG_04365,CNAG_06468" | |
| # use "\n" as the separator so it shows correctly in the text area | |
| input_gene_ids = input_gene_ids.replace(",", "\n") | |
| if "coexp_score_threshold" in query_params.keys(): | |
| coexp_score_threshold = query_params["coexp_score_threshold"] | |
| else: | |
| coexp_score_threshold = "0.85" | |
| if "max_per_gene" in query_params.keys(): | |
| max_per_gene = query_params["max_per_gene"] | |
| else: | |
| max_per_gene = "25" | |
| st.markdown(""" | |
| # CryptoCEN Network | |
| **CryptoCEN** is a co-expression network for *Cryptococcus neoformans* built on 1,524 RNA-seq runs across 34 studies. | |
| A pair of genes are said to be co-expressed when their expression is correlated across different conditions and | |
| is often a marker for genes to be involved in similar processes. | |
| To Cite: | |
| O'Meara MJ, Rapala JR, Nichols CB, Alexandre C, Billmyre RB, Steenwyk JL, A Alspaugh JA, O'Meara TR | |
| CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair. | |
| PLoS Genet 20(2): e1011158. (2024) https://doi.org/10.1371/journal.pgen.1011158 | |
| * Code available at https://github.com/maomlab/CalCEN/tree/master/vignettes/CryptoCEN | |
| * Full network and dataset: https://huggingface.co/datasets/maomlab/CryptoCEN | |
| ## Plot a network for a set of genes | |
| Put a ``CNAG_#####`` gene_id, one one each row to seed the network | |
| """) | |
| h99_transcript_annotations = datasets.load_dataset( | |
| path = "maomlab/CryptoCEN", | |
| data_files = {"h99_transcript_annotations": "h99_transcript_annotations.tsv"}) | |
| h99_transcript_annotations = h99_transcript_annotations["h99_transcript_annotations"].to_pandas() | |
| top_coexp_hits = datasets.load_dataset( | |
| path = "maomlab/CryptoCEN", | |
| data_files = {"top_coexp_hits": "top_coexp_hits.tsv"}) | |
| top_coexp_hits = top_coexp_hits["top_coexp_hits"].to_pandas() | |
| col1, col2, col3 = st.columns(spec = [0.3, 0.2, 0.5]) | |
| with col1: | |
| input_gene_ids = st.text_area( | |
| label = "Gene IDs", | |
| value = f"{input_gene_ids}", | |
| height = 130, | |
| help = "CNAG Gene ID e.g. CNAG_04365") | |
| with col2: | |
| coexp_score_threshold = st.text_input( | |
| label = "Co-expression threshold [0-1]", | |
| value = f"{coexp_score_threshold}", | |
| help = "Default: 0.85") | |
| try: | |
| coexp_score_threshold = float(coexp_score_threshold) | |
| except: | |
| st.error(f"Co-expression threshold should be a number between 0 and 1, instead it is '{coexp_score_threshold}'") | |
| if coexp_score_threshold < 0 or 1 < coexp_score_threshold: | |
| st.error(f"Co-expression threshold should be a number between 0 and 1, instead it is '{coexp_score_threshold}'") | |
| max_per_gene = st.text_input( | |
| label = "Max per gene", | |
| value = f"{max_per_gene}", | |
| help = "Default: 25") | |
| try: | |
| max_per_gene = int(max_per_gene) | |
| except: | |
| st.error(f"Max per gene should be a number greater than 0, instead it is '{max_per_gene}'") | |
| if max_per_gene <= 0: | |
| st.error(f"Max per gene should be a number greater than 0, instead it is '{max_per_gene}'") | |
| ################################## | |
| # Parse and check the user input # | |
| ################################## | |
| seed_gene_ids = [] | |
| for input_gene_id in input_gene_ids.split("\n"): | |
| gene_id = input_gene_id.strip() | |
| if gene_id == "": | |
| continue | |
| else: | |
| seed_gene_ids.append(gene_id) | |
| neighbors = [] | |
| for seed_gene_id in seed_gene_ids: | |
| hits = top_coexp_hits[ | |
| (top_coexp_hits.gene_id_1 == seed_gene_id) & (top_coexp_hits.coexp_score > coexp_score_threshold)] | |
| if len(hits.index) > max_per_gene: | |
| hits = hits[0:max_per_gene] | |
| neighbors.append(hits) | |
| neighbors = pd.concat(neighbors) | |
| neighbor_gene_ids = list(set(neighbors.gene_id_2)) | |
| gene_ids = seed_gene_ids + neighbor_gene_ids | |
| gene_types = ['seed'] * len(seed_gene_ids) + ['neighbor'] * len(neighbor_gene_ids) | |
| cnag_ids = [] | |
| gene_products = [] | |
| descriptions = [] | |
| for gene_id in gene_ids: | |
| try: | |
| cnag_id = h99_transcript_annotations.loc[h99_transcript_annotations["gene_id"] == gene_id]["cnag_id"].values[0] | |
| gene_product = h99_transcript_annotations.loc[h99_transcript_annotations["gene_id"] == gene_id]["gene_product"].values[0] | |
| description = h99_transcript_annotations.loc[h99_transcript_annotations["gene_id"] == gene_id]["description"].values[0] | |
| except: | |
| st.error(f"Unable to locate cnag_id for Gene ID: '{gene_id}', it should be of the form 'cnag_#####'") | |
| cnag_id = None | |
| gene_product = None | |
| description = None | |
| cnag_ids.append(cnag_id) | |
| gene_products.append(gene_product) | |
| descriptions.append(description) | |
| node_info = pd.DataFrame({ | |
| "gene_index": range(len(gene_ids)), | |
| "gene_id" : gene_ids, | |
| "gene_type" : gene_types, | |
| "cnag_id": cnag_ids, | |
| "gene_product": gene_products, | |
| "description": description}) | |
| neighbors = neighbors.merge( | |
| right = node_info, | |
| left_on = "gene_id_1", | |
| right_on = "gene_id") | |
| neighbors = neighbors.merge( | |
| right = node_info, | |
| left_on = "gene_id_2", | |
| right_on = "gene_id", | |
| suffixes = ("_a", "_b")) | |
| ################################ | |
| # Use NetworkX to layout graph # | |
| ################################ | |
| # note I think CytoscapeJS can layout graphs | |
| # but I'm unsure how to do it through the streamlit-cytoscapejs interface :( | |
| st.write(neighbors) | |
| G = nx.Graph() | |
| for i in range(len(neighbors.index)): | |
| edge = neighbors.iloc[i] | |
| G.add_edge( | |
| edge["gene_index_a"], | |
| edge["gene_index_b"], | |
| weight = edge["coexp_score"]) | |
| layout = nx.spring_layout(G) | |
| node_color_lut = { | |
| "seed" : "#4866F0", # blue | |
| "neighbor" : "#F0C547" # gold | |
| } | |
| elements = [] | |
| singleton_index = 0 | |
| for i in range(len(node_info.index)): | |
| node = node_info.iloc[i] | |
| if node["gene_index"] in layout.keys(): | |
| layout_x = layout[node["gene_index"]][0] * 600 + 1500/2 | |
| layout_y = layout[node["gene_index"]][1] * 600 + 1500/2 | |
| else: | |
| layout_x = (singleton_index % 8) * 150 + 100 | |
| layout_y = np.floor(singleton_index / 8) * 50 + 30 | |
| singleton_index += 1 | |
| elements.append({ | |
| "data": { | |
| "id": node["gene_id"], | |
| "label": node["gene_product"] if node["gene_product"] is not None else node["gene_id"], | |
| "color": node_color_lut[node["gene_type"]]}, | |
| "position": { | |
| "x" : layout_x, | |
| "y" : layout_y}}) | |
| for i in range(len(neighbors.index)): | |
| edge = neighbors.iloc[i] | |
| elements.append({ | |
| "data" : { | |
| "source" : edge["gene_id_1"], | |
| "target" : edge["gene_id_2"], | |
| "width" : | |
| 20 if edge["coexp_score"] > 0.99 else | |
| 15 if edge["coexp_score"] > 0.96 else | |
| 10 if edge["coexp_score"] > 0.94 else | |
| 8 if edge["coexp_score"] > 0.89 else | |
| 5}}) | |
| with col3: | |
| st.text('') # help alignment with input box | |
| st.download_button( | |
| label = "Download as as TSV", | |
| data = neighbors.to_csv(sep ='\t').encode('utf-8'), | |
| file_name = f"CryptoCEN_network.tsv", | |
| mime = "text/csv") | |
| ########################################################## | |
| stylesheet = [ | |
| {"selector": "node", "style": { | |
| "width": 140, | |
| "height": 30, | |
| "shape": "rectangle", | |
| "label" : "data(label)", | |
| "labelFontSize": 100, | |
| 'background-color': 'data(color)', | |
| "text-halign": "center", | |
| "text-valign": "center", | |
| }}, | |
| {"selector": "edge", "style": { | |
| "width": "data(width)" | |
| }} | |
| ] | |
| st.title("ToxoCEN Network") | |
| clicked_elements = st_cytoscapejs( | |
| elements = elements, | |
| stylesheet = stylesheet, | |
| width = 1000, | |
| height= 1000, | |
| key = "1") | |